Single-nucleotide polymorphisms (SNPs) selection and identification are the most important tasks in Genome-wide association data analysis. The problem is difficult because genome-wide association data is very high dimensional and a large portion of SNPs in the data is irrelevant to the disease. Advanced machine learning methods have been successfully used in Genome-wide association studies (GWAS) for identification of genetic variants that have relatively big effects in some common, complex diseases. Among them, the most successful one is Random Forests (RF). Despite of performing well in terms of prediction accuracy in some data sets with moderate size, RF still suffers from working in GWAS for selecting informative SNPs and building accurate prediction models. In this paper, we propose to use a new two-stage quality-based sampling method in random forests, named ts-RF, for SNP subspace selection for GWAS. The method first applies p-value assessment to find a cut-off point that separates informative and irrelevant SNPs in two groups. The informative SNPs group is further divided into two sub-groups: highly informative and weak informative SNPs. When sampling the SNP subspace for building trees for the forest, only those SNPs from the two sub-groups are taken into account. The feature subspaces always contain highly informative SNPs when used to split a node at a tree.

With the ever-increasing volume of data, alternative strategies are required to divide big data into statistically consistent data blocks that can be used directly as representative samples of the entire data set in big data analysis. In this paper, we propose the Random Sample Partition (RSP) distributed data model to represent a big data set as a set of disjoint data blocks, called RSP blocks. Each RSP block has a probability distribution similar to that of the entire data set. RSP blocks can be used to estimate the statistical properties of the data and build predictive models without computing the entire data set. We demonstrate the implications of the RSP model on sampling from big data and introduce a new RSP-based method for approximate big data analysis which can be applied to different scenarios in the industry. This method significantly reduces the computational burden of big data and increases the productivity of data scientists

Clustering of customer transaction data is an important procedure to analyze customer behaviors in retail and e-commerce companies. Note that products from companies are often organized as a product tree, in which the leaf nodes are goods to sell, and the internal nodes (except root node) could be multiple product categories. Based on this tree, we present a “personalized product tree“, named purchase tree, to represent a customer's transaction records. So the customers' transaction data set can be compressed into a set of purchase trees. We propose a partitional clustering algorithm, named PurTreeClust, for fast clustering of purchase trees. A new distance metric is proposed to effectively compute the distance between two purchase trees. To cluster the purchase tree data, we first rank the purchase trees as candidate representative trees with a novel separate density, and then select the top k customers as the representatives of k customer groups. Finally, the clustering results are obtained by assigning each customer to the nearest representative. We also propose a gap statistic based method to evaluate the number of clusters. A series of experiments were conducted on ten real-life transaction data sets, and experimental results show the superior performance of the proposed method.

As the volume of data grows rapidly, storing big data in a single data center is no longer feasible. Hence, companies have developed two scenarios to store their big data in multiple data centers. In the first scenario, the company’s big data are distributed in multiple data centers without data replication. In the second scenario, data are also stored in multiple data centers but important data are replicated in these data centers to increase data safety and availability. However, in these scenarios, analyzing big data distributed in multiple data centers becomes a challenging task. In this paper, we propose two data distribution strategies to support big data analysis across geo-distributed data centers. In these strategies, we use the recent Random Sample Partition data model to convert big data into sets of random sample data blocks and distribute these data blocks into multiple data centers either without replication or with replication. In analyzing big data in multiple data centers without replication, we randomly select samples of data blocks from multiple data centers and download the sample data blocks to one data center for analysis. In the second strategy with replication of data blocks, we can analyze big data on any data center by randomly selecting a sample of data blocks replicated from other data centers. This strategy avoids data transformation between data centers. We demonstrate the performance of the two strategies in big data analysis by using simulation results produced on one local data center and four AWS data centers in North America, Asia, and Australia.